3 Hong Kong Mobile Market Users Behavioral Pattern Mobile Campaign Optimization Case Study Men s Shaving Product Case Study Mobile Game App Download
4 Hong Kong Mobile Market
5 Android Vs ios Android 62.0% ipad 3.5% iphone 34.5% 62% Android Compare to the last quarter, the share of android users shows a drop from 80% to 62%, and it possibly reflects that some of the android users switch to iphone The growth of iphone is contributed to the new iphone 6 series and the iphones starts to regains its large screen phone market. Source: UU, Q1, 2015, Vpon Inc.
6 Top 10 Devices iphone 6 iphone 5S Samsung Galaxy Note3 5.7 iphone 6Plus 5.5 iphone Samsung Galaxy Note2 iphone5 Samsung Galaxy S3 Xiaomi Redmi Note being the most influential rival to Android phones, four of the iphone models hits top six. In contrast, the Android phones are highly fragmented, the number of unique users is diluted by different phone manufacturers Samsung Galaxy Note LG G3 5.5 Unique Users Source: UU, Q1, 2015, Vpon Inc.
7 Top 10 Android Devices Samsung Galaxy Note3 5.7 SAMSUNG Samsung Galaxy Note2 Samsung Galaxy S3 Xiaomi Redmi Note remains the market leader and 5 of its phones hits top 10, its board portfolio caters to the fragmented screen size market Samsung Galaxy Note 4 LG G3 Samsung Galaxy S LG earns a major share of the market by its phablet phones after Samsung. G3 is the most popular among the portfolio Sony Xperia Z1 5.0 PHABLET LG G Pro2 LG G top 10 phones are Phablets whose screen size are within The screen size of the top phablet models lie between Unique Users Source: UU, Q1, 2015, Vpon Inc.
8 ios Devices iphone % iphone % iphone 4 5.1% ipad 8% ipad Mini 6% iphone % iphone 6 and iphone 6Plus were released on 19 Sep, 2014, soon it becomes the most popular ios devices and it is still growing It tells that the large screen size strategies fits perfectly to Hong Kong users and it makes a success. Within the iphone 6/6Plus users, 43% users are using the 5.5 iphone 6 Plus which is slightly fewer than that of the standard 4.7 iphone 6 Source: UU, Q1, 2015, Vpon Inc.
9 Mobile Browsers Safari 20.4% 41.7% CHROME Chrome takes the biggest share and from the trend observed in 2014, we expect it s growth should continue Android Browser 37.2% Others 1% Chrome Mobile 41.7% Source: UU, Q1, 2015, Vpon Inc.
10 Hong Kong 18 Districts Traffic Eastern 5% Wan Chai 6% Southern 2% Central and West 6% Outlying 2% Kowloon City 10% Sai Kung 5% Kwai Tsing 8% Yau Tsim Mong 7% Tuen Wan 4% Sham Shiu Po 7% Kwun Tong 6% Wong Tai Sin 3% Yuen Long 5% Sha Tin 9% Tuen Mun 7% Northern 3% Tai Po 5% 48.2% N.T New Territories shares near half of the traffic, Kowloon shares 33% while Hong Kong Island shares 18.8% Source: UU, Q1, 2015, Vpon Inc.
11 Simplified Chinese Apps Location-Based Traffic 54% in 7 DISTRICTS Central and Western, Yuen Long and Yau Tsim Mong, Shatin, Kowloon City, Kwai Tsing and Tsuen Wan are districts sharing top traffic which is expected to be generated by the mainland tourists Source: UU, Q1, 2015, Vpon Inc.
12 Network Operators China Mobile 21.2% Hutchison 18.1% 38% csl. csl. 38.0% Others 2.7% SmarTone 20.0% Since the merge of CSL and PCCW, it becomes the biggest player among all Each of four operators provides 4G LTE services Source: UU, Q1, 2015, Vpon Inc.
13 Users Behavioral Pattern
14 Mobile Internet Access 0% 25% 50% 75% 100% 61% 39% Cellular 40% 50% 60% 50% 41% 59% 53% 47% Wifi 54% 46% 52% 48% 54% 46% 53% 47% 61% Video on Wifi Users prefers to use wifi for apps in video category while to use cellular for apps News and Life categories Video Entertainment News Life Social Education Finance Technology Travel Source: UU, Q1, 2015, Vpon Inc.
15 Hourly Traffic by Carriers Android ipad iphone PEAK HOURS at 1300, 1800 and 2200 Android and iphone traffic spikes at lunch time at 12 am - 1 pm and night time 8-10 pm ipad traffic increases drastically from 8-12 pm and in night time Source: UU, Q1, 2015, Vpon Inc.
16 Android Apps Categories Video 36.1% 36.1% Video Others 2.7% Life 14.8% News 16.8% Entertainment 29.6% Video, Entertainment and News categories takes the major share, 55% of Android Traffic Source: UU, Q1, 2015, Vpon Inc.
17 iphone Apps Categories Video 40.1% 40.1% Video News 43.2% With the larger phone screen size iphone 6/6Plus, video traffic shows a significant increase. It also reveals the potential for more video ad on iphone Others 1% Entertainment 4.3% Life 11.6% Source: UU, Q1, 2015, Vpon Inc.
18 ipad Apps Categories Video 65.3% 65.3% Video Most of ipad users download video Apps for Taiwan, Korea, China and Japan TV series or movies Others 12.0% Entertainment 12.5% Life 10.3% The large screen allows better video and gaming experience and therefore the two category shares almost more than 77% traffic Source: UU, Q1, 2015, Vpon Inc.
19 d Mobile Campaign Optimization
20 Three Influential Factors to Advertising Effectiveness
21 Mobile Advertising Effectiveness Hinges on Consumer s Scenario
22 Case Study Men s Shaving Product
23 Pre-optimization: Observation on different categories performance 1 Daily CTR(%) on Various Categories - Banner Category A Category B Category C Category D 13 Daily CTR(%) on Various Categories - Insterstitial Category A Category B Category C Category D Campaign Run Time/Day Campaign Run Time/Day First step to optimization is to observe, the campaign was subjected to no specific targeting and to all app categories. 6 days was allowed for observation and formulating lookalike audience strategies. Category B was selected for banner optimization and Category B and C were selected for interstitial optimization based on the outstanding CTR performance. Source: CTR, 2015, Vpon Inc.
24 Pilot Run of Two Lookalike Audience Optimization 1.4 Daily CTR(%) of Two Lookalike Audience - Banner Lookalike Audience A Lookalike Audience B Lookalike Audience A Lookalike Audience B Campaign Run Time/Day The CTR performance hinges on the unique behavioral patterns of audience, and hence performance was track and compared. Lookalike audience A whose CTR average at 0.78% was 1.6 times better than normal ad allocation (non-specific audience) whose CTR average at 0.3%. Due to the instability of the CTR of lookalike audience B, CTR fluctuation over a wide range across days, optimization on lookalike audience B was forgone. Such lookalike optimization was not adopted in interstitial since category optimization outperformed its lookalike optimization. Source: CTR, 2015, Vpon Inc.
25 Optimized Allocation Daily CTR of Banner Pre-optimization: observation Pilot-Run on two lookalike audiences Optimized Allocation Campaign Run Time/Day The step-wise optimization takes times, 10 days in this case, to formulate its best fit strategies - prioritizing ad allocation to the combination of the best performing app categories and best lookalike audience A. At the period of optimized allocation, the average CTR is 1.07% which 2.6 times better than non-specific targeting. Source: CTR, Q1 2015, Vpon Inc.
26 Optimized Allocation Daily CTR of Interstitial Pre-optimization; observation Pilot-Run on two lookalike audience Optimized Allocation Campaign Run Time/Day Same process like banner optimization was gone through in interstitial, yet lookalike audiences were forgone, the optimization was formulated by prioritizing ad allocation to the best performing app category and the campaign was continually optimized. At the period of optimized allocation, the average CTR is 10.22% which 2.4 times better than non-specific targeting. Source: CTR, Q1 2015, Vpon Inc.
27 Case Study Mobile Game App Download
28 Campaign Optimization Trilogy: Data Collection-Multi-Audience Sample-Strategy Adjustment Incentive ads
29 App Retention Rate Should Come First than App Download Rate Incentive ads
30 Simultaneous Delivery of Multiple Ad Material; Profound Insight to Consumer s Preference
31 Maximize Campaign Effectiveness According to Different Scenarios
32 Formulate Campaign Strategy Based on Observation of Consumers Interests and Habits
33 Campaign Optimization by Big Data